
import pandas as pd
import numpy as np
import cufflinks as cf
import chart_studio.plotly as py
import seaborn as sns
import plotly.express as px
%matplotlib inline

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
cf.go_offline()


arr_1 = np.random.rand(50,4)
df_1 = pd.DataFrame(arr_1, columns=['A','B','C','D'])
df_1.head()


# old way of doing it
df_1.plot()

<AxesSubplot:>


# drastically better with plotly
df_1.iplot()


import plotly.graph_objects as go
df_stock = px.data.stocks()
px.line(df_stock, x='date', y='GOOG', labels={'x':'Date', 'y':'Price'})


px.line(df_stock, x='date', y=['GOOG','AAPL'],
        labels={'x':"DATE",'y':"PRICES"}, title="Apple versus Google")


fig = go.Figure()
fig.add_trace(go.Scatter(x=df_stock['date'], y=df_stock['AAPL'], mode='lines', name='Apple'))
fig.add_trace(go.Scatter(x=df_stock['date'], y=df_stock['AMZN'], mode='lines', name='Amazon'))
fig.add_trace(go.Scatter(x=df_stock['date'], y=df_stock['GOOG'], mode='lines', name='Google',
                         line=dict(color='firebrick',  width=2,dash="dashdot")))

fig.update_layout(title="Stock Price Data 2018 - 2020", xaxis_title="Price", yaxis_title="Date")


fig.update_layout(xaxis=dict(showline=True, showgrid=False, showticklabels=True,
                            linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside',
                            tickfont=dict(family='Arial', size=12, color="rgb(82, 82, 82)")),
                  yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                 autosize=False,
                 margin = dict(
                 autoexpand = False, l=100, r=20, t=110,),
                 showlegend = False, plot_bgcolor='white')


df_us = px.data.gapminder().query("country == 'United States'")
px.bar(df_us, x='year', y='pop')


df_tips = px.data.tips()
px.bar(df_tips, x='day', y='tip', color='sex',
       title='Tips by sex on Each day', labels={'tip': 'Tip Amount', 'day': 'Day of the Week'})


px.bar(df_tips, x='sex', y='total_bill', color='smoker', barmode='group')


df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
df_europe.head()


fig = px.bar(df_europe, y='pop', x='country', text='pop', color='country')
fig


fig.update_traces(texttemplate="%{text:.2s}", textposition='outside')
fig.update_layout(uniformtext_minsize=8, xaxis_tickangle=-45)


df_iris = px.data.iris()
px.scatter(df_iris, x='sepal_width', y='sepal_length', color='species', size='petal_length', hover_data=['petal_width'])


fig = go.Figure()
fig.add_trace(go.Scatter(x=df_iris['sepal_width'], y=df_iris['sepal_length'],
                        mode='markers', marker_color=df_iris['sepal_width'],
                        text=df_iris['species'], marker=dict(showscale=True)))
fig.update_traces(marker_line_width=2, marker_size=10)


fig = go.Figure(
    data=go.Scattergl(
        x=np.random.randn(100000),
        y=np.random.randn(100000),
        mode = 'markers',
        marker=dict(
        color=np.random.randn(100000),
        colorscale='Viridis',
        line_width=1)))
fig


# showing that you can stack queries, but if its in one place best to put it in one query
df_asia = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
df_asia.head()


px.pie(df_asia, values="pop", names='country',
       title="Population of Countries in Asia",
       #https://plotly.com/python/builtin-colorscales/
       color_discrete_sequence=px.colors.sequential.RdBu)


colors = ['blue', 'green', 'black', 'purple', 'red', 'brown']
fig = go.Figure(data=[go.Pie(labels=['Water', 'Grass', 'Normal', 'Psychic', 'Fire', 'Ground'], values=[110,90,80,80,70,60])])
fig.update_traces(hoverinfo='label+percent', textfont_size=20, textinfo='label+percent', pull=[0.1, 0, 0.2, 0, 0, 0],
                 marker=dict(colors=colors, line=dict(color="#FFFFFF", width=2)))
fig.show()


# rolling two dice
dice_1 = np.random.randint(1,7,5000)
dice_2 = np.random.randint(1,7,5000)
dice_sum = dice_1 + dice_2

fig = px.histogram(dice_sum, nbins=11, labels={'value': "Dice Role"},
                  title = "5000 Dice Roll Histogram", marginal='violin',
                  color_discrete_sequence=['blue'])

fig


fig.update_layout(
    xaxis_title_text = "Dice Roll",
    yaxis_title_text = "Dice Sum",
    bargap=0.2, showlegend=False
)


# stack with diff color
df_tips = px.data.tips()
px.histogram(df_tips, x="total_bill", color="sex")


# continue using df_tips
px.box(df_tips, x='sex', y='tip', points='all')


px.box(df_tips, x='day', y='tip', color = 'sex')


fig = go.Figure()
fig.add_trace(go.Box(
    x=df_tips['sex'],
    y=df_tips['tip'],
    marker_color='blue',
    boxmean='sd'
))


df_stocks = px.data.stocks()
fig = go.Figure()

fig.add_trace(go.Box(
    y=df_stocks['GOOG'], boxpoints='all', name="Google",
    fillcolor="blue", jitter=0.5, whiskerwidth=0.2
))


fig.add_trace(go.Box(
    y=df_stocks['AAPL'], boxpoints='all', name="Apple",
    fillcolor="red", jitter=0.5, whiskerwidth=0.2
))


fig.update_layout(title="Google vs Apple",
                  yaxis=dict(gridcolor="rgb(255, 255, 255)",
                           gridwidth=3),
                  paper_bgcolor="rgb(243, 243, 243)",
                  plot_bgcolor="rgb(243, 243, 243)"
                 )


px.violin(df_tips, y='total_bill', box=True, points='all')


px.violin(df_tips, y='tip', x='smoker', color="sex", box=True, points='all',
         hover_data=df_tips.columns)


fig = go.Figure()
fig.add_trace(go.Violin(x=df_tips['day'][df_tips['smoker'] == 'Yes'],
                       y=df_tips['total_bill'][df_tips['smoker'] == 'Yes'],
                       legendgroup='Yes', scalegroup='Yes', name='Yes',
                       side='negative', line_color='blue'))
fig.add_trace(go.Violin(x=df_tips['day'][df_tips['smoker'] == 'No'],
                       y=df_tips['total_bill'][df_tips['smoker'] == 'No'],
                       legendgroup='Yes', scalegroup='Yes', name='No',
                       side='positive', line_color='red'))
#fig.update_layout()


flights = sns.load_dataset('flights')
flights.head()


fig = px.density_heatmap(flights, x='year', y='month',
                        z='passengers',
                        color_continuous_scale='Viridis')
fig


fig = px.density_heatmap(flights, x='year', y='month',
                        z='passengers',
                        marginal_x='histogram', marginal_y='histogram')
fig


fig = px.scatter_3d(flights, x='year', y='month', z='passengers',
                   color='year', opacity=0.7)
fig


fig = px.line_3d(flights, x='year', y='month', z='passengers',
                   color='year')
fig


fig = px.scatter_matrix(flights, color='month')
fig


df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations='iso_alpha', color='continent',
                     hover_name="country",size='pop', projection='orthographic')
fig


from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)
    
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv',
                dtype={'fips' : str})
df.head()


fig = px.choropleth(df, geojson=counties, locations='fips', color='unemp',
                   color_continuous_scale="Viridis", range_color=(0,12),
                   scope="usa", labels={'unemp':'Unemployment rate'})
fig


df_wind = px.data.wind()
px.scatter_polar(df_wind, r='frequency', theta='direction',
                color='strength', size='frequency', symbol='strength')


px.line_polar(df_wind, r='frequency', theta='direction',
                color='strength', line_close=True, template='plotly_dark')


df_exp = px.data.experiment()
px.scatter_ternary(df_exp, a='experiment_1', b='experiment_2', c='experiment_3', hover_name='group', color='gender')


px.scatter(df_tips, x='total_bill', y='tip', color='smoker',
          facet_col='sex')


px.histogram(df_tips, x='total_bill', y='tip', color='sex', facet_row='time', facet_col='day',
            category_orders={'day':['Thurs', 'Fri', 'Sat', 'Sun'], 'time':['Lunch', 'Dinner']})


df_att = sns.load_dataset('attention')
fig = px.line(df_att, x='solutions', y='score', facet_col='subject',
             facet_col_wrap=5, title='Scores Based on Attention')
fig


df_cnt = px.data.gapminder()
px.scatter(df_cnt, x='gdpPercap', y='lifeExp', animation_frame='year', animation_group='country',
           size='pop', color='continent', hover_name='country', log_x=True, size_max=55, range_x=[100, 100000],
          range_y=[25, 100])


px.bar(df_cnt, x='continent', y='pop', color='continent',
       animation_frame='year', animation_group='country', range_y=[0,4000000000])

	A	B	C	D
0	0.209666	0.995988	0.560780	0.801502
1	0.423231	0.590027	0.691960	0.719189
2	0.989418	0.755647	0.369323	0.554502
3	0.041311	0.273726	0.167763	0.917432
4	0.725611	0.487984	0.990037	0.804366

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
23	Albania	Europe	2007	76.423	3600523	5937.029526	ALB	8
83	Austria	Europe	2007	79.829	8199783	36126.492700	AUT	40
119	Belgium	Europe	2007	79.441	10392226	33692.605080	BEL	56
155	Bosnia and Herzegovina	Europe	2007	74.852	4552198	7446.298803	BIH	70
191	Bulgaria	Europe	2007	73.005	7322858	10680.792820	BGR	100

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
11	Afghanistan	Asia	2007	43.828	31889923	974.580338	AFG	4
95	Bahrain	Asia	2007	75.635	708573	29796.048340	BHR	48
107	Bangladesh	Asia	2007	64.062	150448339	1391.253792	BGD	50
227	Cambodia	Asia	2007	59.723	14131858	1713.778686	KHM	116
299	China	Asia	2007	72.961	1318683096	4959.114854	CHN	156

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121

	fips	unemp
0	01001	5.3
1	01003	5.4
2	01005	8.6
3	01007	6.6
4	01009	5.5

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